MlProjects

BigMart Sales Prediction

https://github.com/hemanitekwani/MlProjects/blob/main/Bigmartsales.ipynb

This repository contains code for predicting sales in a BigMart store using machine learning. The project involves data preprocessing, exploratory data analysis, feature engineering, and training an XGBoost regression model.

Dataset

The dataset used in this project is loaded from a CSV file named Train.csv. It contains information about various features related to items in the store and their corresponding sales.

Features

The dataset includes the following features:

• Item_Identifier
• Item_Weight
• Item_Fat_Content
• Item_Visibility
• Item_Type
• Item_MRP
• Outlet_Identifier
• Outlet_Establishment_Year
• Outlet_Size
• Outlet_Location_Type
• Outlet_Type
• Item_Outlet_Sales

Preprocessing

The dataset is preprocessed in the following steps:

• Handling missing values in Item_Weight and Outlet_Size columns
• Exploratory data analysis using visualizations to understand the distribution of features

Feature Engineering

Label encoding is applied to categorical features to convert them into numerical format for model training.

Model Training

An XGBoost Regressor model is trained to predict the Item_Outlet_Sales:

• Data is split into training and testing sets.
• XGBoost Regressor is initialized and trained on the training data.
• R-squared values are calculated to evaluate model performance on both training and testing data.

Dependencies

• NumPy
• Pandas
• Matplotlib
• Seaborn
• scikit-learn
• XGBoost

Usage

1. Clone the repository.
2. Install the required dependencies.
3. Run the provided Python script to predict BigMart sales.

Customer Segmentation using K-Means Clustering

https://github.com/hemanitekwani/MlProjects/blob/main/customer%20segmentation.ipynb

Introduction

This project aims to perform customer segmentation using the K-Means clustering algorithm based on customers' annual income and spending score. The goal is to identify distinct customer groups for targeted marketing strategies.

Dataset

The dataset used in this project contains customer information including gender, age, annual income, and spending score. It consists of a total of 200 data points.

Dependencies

To run this project, you will need the following dependencies:

• NumPy
• Pandas
• Matplotlib
• Seaborn
• scikit-learn

Usage

Follow these steps to use the project:

1. Clone this repository to your local machine.
2. Navigate to the project directory.
3. Ensure that the required dependencies are installed.
4. Run the Jupyter Notebook or Python script to perform customer segmentation.

Data Exploration

The dataset is loaded and explored to understand its structure and characteristics. Initial analysis confirms the absence of missing values.

K-Means Clustering

The K-Means algorithm is applied to segment the customers into distinct groups. The optimal number of clusters is determined using the within-cluster sum of squares (WCSS) method. The elbow point in the WCSS graph indicates the suitable number of clusters, which in this case is found to be 5.

Visualization

Customer segments are visualized on a 2D scatter plot using their annual income and spending score. Each cluster is assigned a unique color, and cluster centroids are highlighted in cyan. This visualization helps in understanding the different customer groups created by the K-Means algorithm.

Conclusion

By utilizing K-Means clustering, customers are effectively grouped into segments based on their spending behaviors and annual income. This segmentation can guide targeted marketing campaigns and personalized customer interactions.

For more detailed information, refer to the Jupyter Notebook or Python script provided in this repository. Explore the code and visualize the results to gain valuable insights into customer segmentation!